1. Field
The following description relates to an automatic translation apparatus and method to generate a translation model and an apparatus and method for automatic translation, all of which are robust to a voice recognition error.
2. Description of Related Art
Automatic translation may be classified into three processes, voice recognition, automatic translation, and voice synthesis. Because each process is consecutively performed, an error generated in a preceding process affects the following process. In a case of the voice recognition, because there may be various speech production forms depending on a user and a noise level may vary according to a surrounding environment when a voice is input, ways to effectively correspond to variations of the voice signal are required.
Furthermore, recently, voice recognition using mechanical learning has rapidly developed due to its capability to effectively correspond to various changes in a voice signal. However, the voice recognition using mechanical learning is still prone to a voice recognition error when a noise level is high or completely different words have similar pronunciations, such errors cause the quality of automatic translation to degrade.